Benchmark: LLMs Handling Multi-Turn Medical Questions That Embed False Premises

arXiv·medium signal

A cs.CL paper (arXiv 2607.12884, ~July 14) evaluates large language models on multi-turn medical conversations where the user's question itself contains an incorrect assumption or misconception — a setting where safe communication requires correcting the premise rather than answering it literally. It isolates a concrete, under-tested failure mode: models that helpfully answer a false-premise question instead of flagging it. The evaluation methodology generalizes beyond medicine to any high-stakes assistant that must resist a user's embedded errors.